Overview

Dataset statistics

Number of variables20
Number of observations295719
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.1 MiB
Average record size in memory160.0 B

Variable types

Numeric20

Alerts

Time (s) is highly overall correlated with CO (ppm) and 2 other fieldsHigh correlation
CO (ppm) is highly overall correlated with Time (s) and 8 other fieldsHigh correlation
Temperature (C) is highly overall correlated with Time (s)High correlation
Heater voltage (V) is highly overall correlated with R4 (MOhm) and 9 other fieldsHigh correlation
R1 (MOhm) is highly overall correlated with R2 (MOhm) and 8 other fieldsHigh correlation
R2 (MOhm) is highly overall correlated with R1 (MOhm) and 5 other fieldsHigh correlation
R3 (MOhm) is highly overall correlated with R1 (MOhm) and 10 other fieldsHigh correlation
R4 (MOhm) is highly overall correlated with Humidity (%r.h.) and 14 other fieldsHigh correlation
R5 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R6 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R7 (MOhm) is highly overall correlated with R1 (MOhm) and 12 other fieldsHigh correlation
R8 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R9 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R10 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R11 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R12 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R13 (MOhm) is highly overall correlated with CO (ppm) and 13 other fieldsHigh correlation
R14 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
Humidity (%r.h.) is highly overall correlated with Time (s) and 2 other fieldsHigh correlation
Flow rate (mL/min) is highly skewed (γ1 = -98.14844307)Skewed
Time (s) is uniformly distributedUniform
Time (s) has unique valuesUnique
CO (ppm) has 32195 (10.9%) zerosZeros

Reproduction

Analysis started2022-12-20 08:18:58.345626
Analysis finished2022-12-20 08:20:51.019064
Duration1 minute and 52.67 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Time (s)
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct295719
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45457.824
Minimum0
Maximum90909.778
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:51.329060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4537.6472
Q122727.738
median45460.834
Q368177.907
95-th percentile86361.607
Maximum90909.778
Range90909.778
Interquartile range (IQR)45450.169

Descriptive statistics

Standard deviation26242.178
Coefficient of variation (CV)0.57728627
Kurtosis-1.1995741
Mean45457.824
Median Absolute Deviation (MAD)22725.154
Skewness-0.00041936424
Sum1.3442742 × 1010
Variance6.8865189 × 108
MonotonicityStrictly increasing
2022-12-20T13:50:51.503280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
60634.674 1
 
< 0.1%
60606.626 1
 
< 0.1%
60606.321 1
 
< 0.1%
60606.017 1
 
< 0.1%
60605.712 1
 
< 0.1%
60605.407 1
 
< 0.1%
60605.102 1
 
< 0.1%
60604.796 1
 
< 0.1%
60604.491 1
 
< 0.1%
Other values (295709) 295709
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.309 1
< 0.1%
0.618 1
< 0.1%
0.926 1
< 0.1%
1.234 1
< 0.1%
1.544 1
< 0.1%
1.854 1
< 0.1%
2.163 1
< 0.1%
2.472 1
< 0.1%
2.781 1
< 0.1%
ValueCountFrequency (%)
90909.778 1
< 0.1%
90909.469 1
< 0.1%
90909.162 1
< 0.1%
90908.853 1
< 0.1%
90908.545 1
< 0.1%
90908.236 1
< 0.1%
90907.927 1
< 0.1%
90907.62 1
< 0.1%
90907.31 1
< 0.1%
90907.003 1
< 0.1%

CO (ppm)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct309
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9007864
Minimum0
Maximum20
Zeros32195
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:51.660205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.44
median8.89
Q315.56
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)11.12

Descriptive statistics

Standard deviation6.4292286
Coefficient of variation (CV)0.64936545
Kurtosis-1.2336219
Mean9.9007864
Median Absolute Deviation (MAD)6.67
Skewness0.0093122168
Sum2927850.7
Variance41.33498
MonotonicityNot monotonic
2022-12-20T13:50:51.822602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32195
10.9%
20 29316
9.9%
6.67 29281
9.9%
2.22 29268
9.9%
15.56 29260
9.9%
8.89 29248
9.9%
13.33 29215
9.9%
17.78 29214
9.9%
11.11 29214
9.9%
4.44 29207
9.9%
Other values (299) 301
 
0.1%
ValueCountFrequency (%)
0 32195
10.9%
0.0622 1
 
< 0.1%
0.0889 1
 
< 0.1%
0.1576 1
 
< 0.1%
0.2131 1
 
< 0.1%
0.3289 1
 
< 0.1%
0.3441 1
 
< 0.1%
0.5333 1
 
< 0.1%
0.64 1
 
< 0.1%
0.8458 1
 
< 0.1%
ValueCountFrequency (%)
20 29316
9.9%
19.8801 1
 
< 0.1%
19.7355 1
 
< 0.1%
19.6048 1
 
< 0.1%
19.4668 1
 
< 0.1%
19.203 1
 
< 0.1%
19.2 1
 
< 0.1%
19.1643 1
 
< 0.1%
18.8634 1
 
< 0.1%
18.5259 1
 
< 0.1%

Humidity (%r.h.)
Real number (ℝ)

Distinct17262
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.966022
Minimum17.5
Maximum71.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:51.996445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum17.5
5-th percentile23.1
Q136.17
median46.67
Q355.33
95-th percentile64.79
Maximum71.96
Range54.46
Interquartile range (IQR)19.16

Descriptive statistics

Standard deviation12.315889
Coefficient of variation (CV)0.26793462
Kurtosis-0.73048547
Mean45.966022
Median Absolute Deviation (MAD)9.43
Skewness-0.20783257
Sum13593026
Variance151.68111
MonotonicityNot monotonic
2022-12-20T13:50:52.158308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 3100
 
1.0%
36.17 2865
 
1.0%
30.73 2531
 
0.9%
52.29 2268
 
0.8%
52.81 2138
 
0.7%
41.4 2109
 
0.7%
22.55 1976
 
0.7%
32.91 1864
 
0.6%
48.69 1779
 
0.6%
37.76 1713
 
0.6%
Other values (17252) 273376
92.4%
ValueCountFrequency (%)
17.5 941
0.3%
17.5005 1
 
< 0.1%
17.5023 1
 
< 0.1%
17.5029 1
 
< 0.1%
17.5035 1
 
< 0.1%
17.5053 1
 
< 0.1%
17.5059 1
 
< 0.1%
17.5066 1
 
< 0.1%
17.5084 1
 
< 0.1%
17.509 1
 
< 0.1%
ValueCountFrequency (%)
71.96 172
0.1%
71.8843 1
 
< 0.1%
71.8842 1
 
< 0.1%
71.863 1
 
< 0.1%
71.8165 1
 
< 0.1%
71.7405 1
 
< 0.1%
71.7358 1
 
< 0.1%
71.7147 1
 
< 0.1%
71.6677 1
 
< 0.1%
71.5972 1
 
< 0.1%

Temperature (C)
Real number (ℝ)

Distinct5625
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.476381
Minimum23.7184
Maximum26.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:52.429522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum23.7184
5-th percentile26.14
Q126.3
median26.46
Q326.62
95-th percentile26.82
Maximum26.94
Range3.2216
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.21164737
Coefficient of variation (CV)0.0079938179
Kurtosis-0.87060446
Mean26.476381
Median Absolute Deviation (MAD)0.16
Skewness0.032809477
Sum7829568.8
Variance0.044794607
MonotonicityNot monotonic
2022-12-20T13:50:52.583847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.22 28561
 
9.7%
26.38 25146
 
8.5%
26.58 24406
 
8.3%
26.46 21398
 
7.2%
26.78 20200
 
6.8%
26.54 18998
 
6.4%
26.7 16359
 
5.5%
26.62 16320
 
5.5%
26.42 15343
 
5.2%
26.14 13690
 
4.6%
Other values (5615) 95298
32.2%
ValueCountFrequency (%)
23.7184 1
 
< 0.1%
26.06 86
< 0.1%
26.0616 1
 
< 0.1%
26.0642 1
 
< 0.1%
26.0648 1
 
< 0.1%
26.0659 1
 
< 0.1%
26.0664 1
 
< 0.1%
26.071 1
 
< 0.1%
26.0717 1
 
< 0.1%
26.072 1
 
< 0.1%
ValueCountFrequency (%)
26.94 175
0.1%
26.9386 1
 
< 0.1%
26.9376 1
 
< 0.1%
26.9374 1
 
< 0.1%
26.9372 1
 
< 0.1%
26.9361 1
 
< 0.1%
26.9351 1
 
< 0.1%
26.932 1
 
< 0.1%
26.9318 1
 
< 0.1%
26.9316 1
 
< 0.1%

Flow rate (mL/min)
Real number (ℝ)

Distinct11390
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.94267
Minimum0
Maximum275.1803
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:52.745542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile239.751
Q1239.8958
median239.9729
Q3240.0462
95-th percentile240.183
Maximum275.1803
Range275.1803
Interquartile range (IQR)0.1504

Descriptive statistics

Standard deviation1.9474977
Coefficient of variation (CV)0.0081165127
Kurtosis11118.611
Mean239.94267
Median Absolute Deviation (MAD)0.0751
Skewness-98.148443
Sum70955606
Variance3.7927473
MonotonicityNot monotonic
2022-12-20T13:50:52.882009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239.962 145
 
< 0.1%
239.9802 144
 
< 0.1%
240.0029 142
 
< 0.1%
239.9577 141
 
< 0.1%
239.9533 134
 
< 0.1%
239.9688 134
 
< 0.1%
239.9815 134
 
< 0.1%
239.9846 134
 
< 0.1%
239.9645 133
 
< 0.1%
239.9667 133
 
< 0.1%
Other values (11380) 294345
99.5%
ValueCountFrequency (%)
0 9
< 0.1%
0.1032 1
 
< 0.1%
0.252 1
 
< 0.1%
0.4003 1
 
< 0.1%
30.9756 1
 
< 0.1%
34.7289 1
 
< 0.1%
46.5131 1
 
< 0.1%
92.5243 1
 
< 0.1%
103.1225 1
 
< 0.1%
108.9746 1
 
< 0.1%
ValueCountFrequency (%)
275.1803 1
< 0.1%
266.0523 1
< 0.1%
263.865 1
< 0.1%
263.2245 1
< 0.1%
260.0188 1
< 0.1%
259.6322 1
< 0.1%
258.3716 1
< 0.1%
255.6549 1
< 0.1%
255.469 1
< 0.1%
254.8473 1
< 0.1%

Heater voltage (V)
Real number (ℝ)

Distinct1721
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35529264
Minimum0.199
Maximum0.901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:53.039716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.199
5-th percentile0.2
Q10.2
median0.2
Q30.207
95-th percentile0.899
Maximum0.901
Range0.702
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.28866321
Coefficient of variation (CV)0.81246606
Kurtosis-0.20685723
Mean0.35529264
Median Absolute Deviation (MAD)0
Skewness1.3367007
Sum105066.78
Variance0.083326448
MonotonicityNot monotonic
2022-12-20T13:50:53.184535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 163234
55.2%
0.899 26996
 
9.1%
0.201 9503
 
3.2%
0.898 5233
 
1.8%
0.202 2888
 
1.0%
0.2006 2641
 
0.9%
0.2002 2637
 
0.9%
0.2003 2589
 
0.9%
0.2004 2588
 
0.9%
0.2007 2584
 
0.9%
Other values (1711) 74826
25.3%
ValueCountFrequency (%)
0.199 758
0.3%
0.1991 1106
0.4%
0.1992 1025
0.3%
0.1993 1066
0.4%
0.1994 1095
0.4%
0.1995 1045
0.4%
0.1996 1007
0.3%
0.1997 1080
0.4%
0.1998 1043
0.4%
0.1999 1020
0.3%
ValueCountFrequency (%)
0.901 2
 
< 0.1%
0.9009 3
 
< 0.1%
0.9008 2
 
< 0.1%
0.9007 2
 
< 0.1%
0.9006 3
 
< 0.1%
0.9005 4
 
< 0.1%
0.9004 2
 
< 0.1%
0.9002 2
 
< 0.1%
0.9001 1
 
< 0.1%
0.9 2515
0.9%

R1 (MOhm)
Real number (ℝ)

Distinct8524
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.962249
Minimum0.0315
Maximum113.4868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:53.346709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0315
5-th percentile0.0781
Q10.4048
median1.6441
Q325.1595
95-th percentile65.4927
Maximum113.4868
Range113.4553
Interquartile range (IQR)24.7547

Descriptive statistics

Standard deviation22.368187
Coefficient of variation (CV)1.4949749
Kurtosis1.0716995
Mean14.962249
Median Absolute Deviation (MAD)1.5622
Skewness1.4854498
Sum4424621.4
Variance500.3358
MonotonicityNot monotonic
2022-12-20T13:50:53.491879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0943 709
 
0.2%
68.5571 696
 
0.2%
69.1448 687
 
0.2%
68.0747 677
 
0.2%
0.094 665
 
0.2%
0.0936 663
 
0.2%
0.0946 657
 
0.2%
0.0944 651
 
0.2%
0.0948 648
 
0.2%
67.0368 646
 
0.2%
Other values (8514) 289020
97.7%
ValueCountFrequency (%)
0.0315 1
 
< 0.1%
0.0322 1
 
< 0.1%
0.0325 1
 
< 0.1%
0.0326 1
 
< 0.1%
0.0327 1
 
< 0.1%
0.0328 1
 
< 0.1%
0.0329 6
< 0.1%
0.0331 1
 
< 0.1%
0.0333 2
 
< 0.1%
0.0334 1
 
< 0.1%
ValueCountFrequency (%)
113.4868 1
 
< 0.1%
111.9292 3
 
< 0.1%
110.6632 1
 
< 0.1%
109.181 4
 
< 0.1%
107.9756 9
 
< 0.1%
106.5634 12
 
< 0.1%
105.4143 29
< 0.1%
104.0673 18
< 0.1%
102.9706 28
< 0.1%
101.6844 38
< 0.1%

R2 (MOhm)
Real number (ℝ)

Distinct8257
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.396103
Minimum0.056
Maximum154.629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:53.646968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.056
5-th percentile0.1393
Q10.4841
median1.3561
Q328.8601
95-th percentile76.9383
Maximum154.629
Range154.573
Interquartile range (IQR)28.376

Descriptive statistics

Standard deviation26.648398
Coefficient of variation (CV)1.5318602
Kurtosis0.52237417
Mean17.396103
Median Absolute Deviation (MAD)1.2148
Skewness1.3960421
Sum5144358.1
Variance710.13712
MonotonicityNot monotonic
2022-12-20T13:50:53.794489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.0683 1057
 
0.4%
75.6194 1033
 
0.3%
78.3034 1009
 
0.3%
74.3444 1000
 
0.3%
76.9383 996
 
0.3%
75.0345 993
 
0.3%
77.677 985
 
0.3%
80.5097 984
 
0.3%
79.7171 983
 
0.3%
76.3332 970
 
0.3%
Other values (8247) 285709
96.6%
ValueCountFrequency (%)
0.056 1
< 0.1%
0.0564 1
< 0.1%
0.0567 1
< 0.1%
0.057 1
< 0.1%
0.0573 1
< 0.1%
0.0574 1
< 0.1%
0.0576 1
< 0.1%
0.0579 1
< 0.1%
0.0581 1
< 0.1%
0.0587 1
< 0.1%
ValueCountFrequency (%)
154.629 1
 
< 0.1%
142.5199 1
 
< 0.1%
140.0805 1
 
< 0.1%
122.8846 1
 
< 0.1%
119.5851 2
 
< 0.1%
116.4568 2
 
< 0.1%
114.818 3
 
< 0.1%
113.4868 2
 
< 0.1%
111.9292 7
< 0.1%
110.6632 13
< 0.1%

R3 (MOhm)
Real number (ℝ)

Distinct8241
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.23308
Minimum0.054
Maximum182.3433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:53.958770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.054
5-th percentile0.1121
Q10.5863
median4.0554
Q345.0994
95-th percentile81.51
Maximum182.3433
Range182.2893
Interquartile range (IQR)44.5131

Descriptive statistics

Standard deviation28.695365
Coefficient of variation (CV)1.2906608
Kurtosis-0.31676391
Mean22.23308
Median Absolute Deviation (MAD)3.9416
Skewness1.0494963
Sum6574744.1
Variance823.424
MonotonicityNot monotonic
2022-12-20T13:50:54.112782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81.51 1130
 
0.4%
83.0507 1128
 
0.4%
85.3974 1109
 
0.4%
83.7703 1073
 
0.4%
84.6501 1072
 
0.4%
82.2033 1066
 
0.4%
80.6932 1065
 
0.4%
80.0247 1021
 
0.3%
78.592 993
 
0.3%
79.2369 981
 
0.3%
Other values (8231) 285081
96.4%
ValueCountFrequency (%)
0.054 1
< 0.1%
0.0563 2
< 0.1%
0.0564 1
< 0.1%
0.0566 1
< 0.1%
0.057 1
< 0.1%
0.0572 1
< 0.1%
0.0573 1
< 0.1%
0.0574 1
< 0.1%
0.0576 1
< 0.1%
0.0578 1
< 0.1%
ValueCountFrequency (%)
182.3433 1
 
< 0.1%
165.1174 1
 
< 0.1%
156.6533 1
 
< 0.1%
151.3177 1
 
< 0.1%
129.2732 1
 
< 0.1%
123.6951 1
 
< 0.1%
114.1263 2
 
< 0.1%
112.8031 6
 
< 0.1%
111.2549 10
 
< 0.1%
109.9965 30
< 0.1%

R4 (MOhm)
Real number (ℝ)

Distinct7567
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.42967
Minimum0.0402
Maximum91.8226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:54.275169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0402
5-th percentile0.1021
Q12.0683
median18.5796
Q329.2098
95-th percentile44.9368
Maximum91.8226
Range91.7824
Interquartile range (IQR)27.1415

Descriptive statistics

Standard deviation15.210454
Coefficient of variation (CV)0.8253243
Kurtosis-0.5933302
Mean18.42967
Median Absolute Deviation (MAD)13.0255
Skewness0.43109156
Sum5450003.6
Variance231.35792
MonotonicityNot monotonic
2022-12-20T13:50:54.519013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.4002 791
 
0.3%
28.8515 783
 
0.3%
29.4463 774
 
0.3%
29.9306 774
 
0.3%
27.8649 770
 
0.3%
29.2098 769
 
0.3%
28.9769 765
 
0.3%
28.7477 764
 
0.3%
33.1964 761
 
0.3%
27.1253 761
 
0.3%
Other values (7557) 288007
97.4%
ValueCountFrequency (%)
0.0402 1
 
< 0.1%
0.0404 1
 
< 0.1%
0.0411 1
 
< 0.1%
0.0413 3
< 0.1%
0.0415 1
 
< 0.1%
0.0418 1
 
< 0.1%
0.0419 3
< 0.1%
0.042 3
< 0.1%
0.0421 3
< 0.1%
0.0422 1
 
< 0.1%
ValueCountFrequency (%)
91.8226 1
 
< 0.1%
75.3221 2
 
< 0.1%
74.5089 1
 
< 0.1%
73.8444 4
 
< 0.1%
73.0622 3
 
< 0.1%
72.4229 10
< 0.1%
71.6701 8
 
< 0.1%
71.0545 24
< 0.1%
70.3295 24
< 0.1%
69.7363 24
< 0.1%

R5 (MOhm)
Real number (ℝ)

Distinct7913
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.054371
Minimum0.0489
Maximum124.1949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:54.663395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0489
5-th percentile0.1151
Q11.7852
median32.317
Q350.562
95-th percentile76.7838
Maximum124.1949
Range124.146
Interquartile range (IQR)48.7768

Descriptive statistics

Standard deviation26.675759
Coefficient of variation (CV)0.85900175
Kurtosis-0.96821217
Mean31.054371
Median Absolute Deviation (MAD)24.8857
Skewness0.35223479
Sum9183367.7
Variance711.59613
MonotonicityNot monotonic
2022-12-20T13:50:54.819254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.3116 1295
 
0.4%
46.7486 1288
 
0.4%
47.7793 1284
 
0.4%
45.7605 1274
 
0.4%
48.6068 1270
 
0.4%
47.2584 1253
 
0.4%
49.9804 1248
 
0.4%
47.0253 1248
 
0.4%
48.8555 1247
 
0.4%
48.0682 1231
 
0.4%
Other values (7903) 283081
95.7%
ValueCountFrequency (%)
0.0489 1
 
< 0.1%
0.0492 1
 
< 0.1%
0.0497 1
 
< 0.1%
0.0499 1
 
< 0.1%
0.05 2
< 0.1%
0.0502 1
 
< 0.1%
0.0507 1
 
< 0.1%
0.0508 1
 
< 0.1%
0.051 1
 
< 0.1%
0.0513 3
< 0.1%
ValueCountFrequency (%)
124.1949 1
 
< 0.1%
122.6378 2
 
< 0.1%
120.8197 10
 
< 0.1%
119.345 12
 
< 0.1%
117.6218 6
 
< 0.1%
116.223 12
 
< 0.1%
114.5874 14
< 0.1%
113.2589 14
< 0.1%
111.7044 29
< 0.1%
110.441 34
< 0.1%

R6 (MOhm)
Real number (ℝ)

Distinct7861
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.773424
Minimum0.0485
Maximum138.2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:54.971586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0485
5-th percentile0.1244
Q11.5828
median22.9903
Q349.6055
95-th percentile78.4368
Maximum138.2019
Range138.1534
Interquartile range (IQR)48.0227

Descriptive statistics

Standard deviation27.238787
Coefficient of variation (CV)0.9466648
Kurtosis-0.86195025
Mean28.773424
Median Absolute Deviation (MAD)22.8494
Skewness0.55940405
Sum8508848
Variance741.95154
MonotonicityNot monotonic
2022-12-20T13:50:55.115016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.6055 1162
 
0.4%
79.9474 1150
 
0.4%
77.6364 1138
 
0.4%
47.8646 1133
 
0.4%
47.3106 1120
 
0.4%
48.4314 1120
 
0.4%
50.2138 1118
 
0.4%
45.9557 1110
 
0.4%
78.4368 1107
 
0.4%
50.4951 1106
 
0.4%
Other values (7851) 284455
96.2%
ValueCountFrequency (%)
0.0485 1
< 0.1%
0.0486 1
< 0.1%
0.0497 1
< 0.1%
0.0508 1
< 0.1%
0.0509 1
< 0.1%
0.0513 1
< 0.1%
0.0516 1
< 0.1%
0.0521 1
< 0.1%
0.0524 2
< 0.1%
0.0526 1
< 0.1%
ValueCountFrequency (%)
138.2019 1
 
< 0.1%
120.179 2
 
< 0.1%
118.6302 1
 
< 0.1%
116.8232 4
 
< 0.1%
115.3585 5
 
< 0.1%
113.6483 6
 
< 0.1%
112.2611 23
 
< 0.1%
110.6402 28
< 0.1%
109.3244 49
< 0.1%
107.7859 69
< 0.1%

R7 (MOhm)
Real number (ℝ)

Distinct7820
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.675358
Minimum0.0534
Maximum151.012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:55.275463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0534
5-th percentile0.1222
Q11.8844
median31.4054
Q352.4174
95-th percentile79.8631
Maximum151.012
Range150.9586
Interquartile range (IQR)50.533

Descriptive statistics

Standard deviation27.594177
Coefficient of variation (CV)0.87115595
Kurtosis-1.0184431
Mean31.675358
Median Absolute Deviation (MAD)25.7186
Skewness0.3730344
Sum9367005.3
Variance761.43859
MonotonicityNot monotonic
2022-12-20T13:50:55.422242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.2568 1313
 
0.4%
49.4202 1304
 
0.4%
50.8483 1294
 
0.4%
48.8606 1279
 
0.4%
48.3134 1279
 
0.4%
52.7077 1260
 
0.4%
49.6787 1259
 
0.4%
49.1134 1243
 
0.4%
51.1767 1238
 
0.4%
51.7898 1237
 
0.4%
Other values (7810) 283013
95.7%
ValueCountFrequency (%)
0.0534 1
 
< 0.1%
0.0535 2
< 0.1%
0.0536 1
 
< 0.1%
0.0538 4
< 0.1%
0.0539 1
 
< 0.1%
0.054 1
 
< 0.1%
0.0541 2
< 0.1%
0.0543 1
 
< 0.1%
0.0545 2
< 0.1%
0.0547 1
 
< 0.1%
ValueCountFrequency (%)
151.012 1
 
< 0.1%
116.9118 1
 
< 0.1%
115.5214 3
 
< 0.1%
113.8957 3
 
< 0.1%
112.5752 9
 
< 0.1%
111.0301 32
 
< 0.1%
109.7743 51
< 0.1%
108.304 78
< 0.1%
107.1083 95
< 0.1%
105.7075 116
< 0.1%

R8 (MOhm)
Real number (ℝ)

Distinct6283
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.705773
Minimum0.033
Maximum102.8265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:55.582846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.033
5-th percentile0.0992
Q111.2915
median25.8081
Q339.0413
95-th percentile57.2171
Maximum102.8265
Range102.7935
Interquartile range (IQR)27.7498

Descriptive statistics

Standard deviation18.65203
Coefficient of variation (CV)0.72559692
Kurtosis-0.82269483
Mean25.705773
Median Absolute Deviation (MAD)13.4085
Skewness0.13815236
Sum7601685.5
Variance347.89821
MonotonicityNot monotonic
2022-12-20T13:50:55.733443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1002 1016
 
0.3%
43.2879 1001
 
0.3%
42.823 992
 
0.3%
43.982 990
 
0.3%
41.6828 990
 
0.3%
44.4719 978
 
0.3%
42.5736 974
 
0.3%
43.0331 972
 
0.3%
38.833 971
 
0.3%
0.1 964
 
0.3%
Other values (6273) 285871
96.7%
ValueCountFrequency (%)
0.033 1
 
< 0.1%
0.0333 1
 
< 0.1%
0.0338 3
< 0.1%
0.0339 1
 
< 0.1%
0.0344 1
 
< 0.1%
0.0345 1
 
< 0.1%
0.0347 2
< 0.1%
0.0348 3
< 0.1%
0.0349 2
< 0.1%
0.035 1
 
< 0.1%
ValueCountFrequency (%)
102.8265 1
 
< 0.1%
94.5965 1
 
< 0.1%
89.3217 1
 
< 0.1%
87.4055 2
 
< 0.1%
84.7592 2
 
< 0.1%
83.8072 8
< 0.1%
83.03 3
 
< 0.1%
82.1159 3
 
< 0.1%
81.3692 10
< 0.1%
80.4907 9
< 0.1%

R9 (MOhm)
Real number (ℝ)

Distinct6218
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.29542
Minimum0.0292
Maximum84.9877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:55.886493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0292
5-th percentile0.0967
Q18.3757
median20.6641
Q332.7781
95-th percentile49.053
Maximum84.9877
Range84.9585
Interquartile range (IQR)24.4024

Descriptive statistics

Standard deviation16.050916
Coefficient of variation (CV)0.75372618
Kurtosis-0.73337717
Mean21.29542
Median Absolute Deviation (MAD)12.114
Skewness0.27707091
Sum6297460.3
Variance257.63189
MonotonicityNot monotonic
2022-12-20T13:50:56.026703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0971 2899
 
1.0%
0.0972 2792
 
0.9%
0.0973 2702
 
0.9%
0.097 2549
 
0.9%
0.0975 2508
 
0.8%
0.0976 2417
 
0.8%
0.0977 2286
 
0.8%
0.0968 2282
 
0.8%
0.0967 1967
 
0.7%
0.0966 1936
 
0.7%
Other values (6208) 271381
91.8%
ValueCountFrequency (%)
0.0292 1
 
< 0.1%
0.0294 1
 
< 0.1%
0.0296 1
 
< 0.1%
0.0297 1
 
< 0.1%
0.0299 4
< 0.1%
0.0301 1
 
< 0.1%
0.0302 3
< 0.1%
0.0304 5
< 0.1%
0.0306 4
< 0.1%
0.0307 3
< 0.1%
ValueCountFrequency (%)
84.9877 1
 
< 0.1%
74.4511 1
 
< 0.1%
70.1491 2
 
< 0.1%
69.5143 7
 
< 0.1%
68.9939 5
 
< 0.1%
68.3795 5
 
< 0.1%
67.8757 10
 
< 0.1%
67.2807 17
< 0.1%
66.7926 20
< 0.1%
66.2161 30
< 0.1%

R10 (MOhm)
Real number (ℝ)

Distinct6458
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.224047
Minimum0.0366
Maximum134.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:56.175446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0366
5-th percentile0.1175
Q17.4485
median23.1211
Q339.3194
95-th percentile61.9071
Maximum134.999
Range134.9624
Interquartile range (IQR)31.8709

Descriptive statistics

Standard deviation20.22483
Coefficient of variation (CV)0.80180751
Kurtosis-0.69493906
Mean25.224047
Median Absolute Deviation (MAD)16.0349
Skewness0.43444643
Sum7459230
Variance409.04376
MonotonicityNot monotonic
2022-12-20T13:50:56.439146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1193 1934
 
0.7%
0.1195 1891
 
0.6%
0.1194 1867
 
0.6%
0.119 1845
 
0.6%
0.1191 1834
 
0.6%
0.1197 1763
 
0.6%
0.1198 1666
 
0.6%
0.1188 1659
 
0.6%
0.1199 1584
 
0.5%
0.1187 1505
 
0.5%
Other values (6448) 278171
94.1%
ValueCountFrequency (%)
0.0366 1
 
< 0.1%
0.0368 3
< 0.1%
0.0372 2
< 0.1%
0.0373 1
 
< 0.1%
0.0374 2
< 0.1%
0.0375 1
 
< 0.1%
0.0379 2
< 0.1%
0.0384 1
 
< 0.1%
0.0385 1
 
< 0.1%
0.0386 1
 
< 0.1%
ValueCountFrequency (%)
134.999 1
 
< 0.1%
114.1263 1
 
< 0.1%
104.7792 1
 
< 0.1%
93.6563 1
 
< 0.1%
90.6767 3
 
< 0.1%
89.8357 3
 
< 0.1%
88.8467 8
< 0.1%
88.0388 7
< 0.1%
87.0883 8
< 0.1%
86.3116 6
< 0.1%

R11 (MOhm)
Real number (ℝ)

Distinct6192
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.087685
Minimum0.031
Maximum108.8521
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:56.598983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.031
5-th percentile0.1075
Q110.2553
median26.8533
Q341.3344
95-th percentile62.0947
Maximum108.8521
Range108.8211
Interquartile range (IQR)31.0791

Descriptive statistics

Standard deviation20.191156
Coefficient of variation (CV)0.74539985
Kurtosis-0.81259195
Mean27.087685
Median Absolute Deviation (MAD)14.8818
Skewness0.218311
Sum8010343
Variance407.68279
MonotonicityNot monotonic
2022-12-20T13:50:56.751773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1084 3013
 
1.0%
0.1085 2986
 
1.0%
0.1082 2978
 
1.0%
0.1081 2704
 
0.9%
0.1086 2586
 
0.9%
0.108 2452
 
0.8%
0.1078 2387
 
0.8%
0.1077 2006
 
0.7%
0.1088 1901
 
0.6%
0.1075 1708
 
0.6%
Other values (6182) 270998
91.6%
ValueCountFrequency (%)
0.031 1
 
< 0.1%
0.0315 1
 
< 0.1%
0.0316 2
< 0.1%
0.0317 2
< 0.1%
0.0319 2
< 0.1%
0.032 3
< 0.1%
0.0321 1
 
< 0.1%
0.0325 2
< 0.1%
0.0326 2
< 0.1%
0.0327 1
 
< 0.1%
ValueCountFrequency (%)
108.8521 1
 
< 0.1%
96.936 1
 
< 0.1%
88.3056 1
 
< 0.1%
87.3522 1
 
< 0.1%
86.5731 8
 
< 0.1%
85.6562 14
< 0.1%
84.9067 29
< 0.1%
84.0241 34
< 0.1%
83.3024 34
< 0.1%
82.4524 32
< 0.1%

R12 (MOhm)
Real number (ℝ)

Distinct6281
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.833538
Minimum0.0327
Maximum90.2894
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:56.913483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0327
5-th percentile0.1071
Q19.3847
median24.903
Q338.1166
95-th percentile54.7785
Maximum90.2894
Range90.2567
Interquartile range (IQR)28.7319

Descriptive statistics

Standard deviation18.278996
Coefficient of variation (CV)0.73606088
Kurtosis-0.76863944
Mean24.833538
Median Absolute Deviation (MAD)13.7434
Skewness0.17860848
Sum7343749
Variance334.12168
MonotonicityNot monotonic
2022-12-20T13:50:57.058106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1082 1312
 
0.4%
0.1084 1310
 
0.4%
0.1086 1229
 
0.4%
0.1083 1216
 
0.4%
0.108 1214
 
0.4%
0.1087 1160
 
0.4%
0.1088 1156
 
0.4%
0.1091 1096
 
0.4%
0.1079 1084
 
0.4%
0.1093 1067
 
0.4%
Other values (6271) 283875
96.0%
ValueCountFrequency (%)
0.0327 1
 
< 0.1%
0.033 2
< 0.1%
0.0331 1
 
< 0.1%
0.0335 2
< 0.1%
0.0336 1
 
< 0.1%
0.0337 3
< 0.1%
0.0338 3
< 0.1%
0.034 4
< 0.1%
0.0341 1
 
< 0.1%
0.0342 3
< 0.1%
ValueCountFrequency (%)
90.2894 1
 
< 0.1%
84.1934 1
 
< 0.1%
83.4702 8
 
< 0.1%
82.6184 14
 
< 0.1%
81.9217 15
 
< 0.1%
81.1007 21
 
< 0.1%
80.4289 30
< 0.1%
79.6371 45
< 0.1%
78.9889 39
< 0.1%
78.2247 59
< 0.1%

R13 (MOhm)
Real number (ℝ)

Distinct6361
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.742677
Minimum0.033
Maximum75.4135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:57.208472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.033
5-th percentile0.1003
Q17.5149
median20.6614
Q333.3976
95-th percentile51.5801
Maximum75.4135
Range75.3805
Interquartile range (IQR)25.8827

Descriptive statistics

Standard deviation16.783496
Coefficient of variation (CV)0.77191489
Kurtosis-0.65978483
Mean21.742677
Median Absolute Deviation (MAD)12.8567
Skewness0.35484716
Sum6429722.6
Variance281.68574
MonotonicityNot monotonic
2022-12-20T13:50:57.355083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1022 1235
 
0.4%
0.1021 1184
 
0.4%
0.1019 1166
 
0.4%
0.1025 1141
 
0.4%
0.1018 1138
 
0.4%
0.1027 1128
 
0.4%
0.1023 1125
 
0.4%
0.1017 1104
 
0.4%
0.1026 1043
 
0.4%
0.1029 1041
 
0.4%
Other values (6351) 284414
96.2%
ValueCountFrequency (%)
0.033 1
 
< 0.1%
0.0342 2
< 0.1%
0.0343 2
< 0.1%
0.0344 2
< 0.1%
0.0345 2
< 0.1%
0.0346 2
< 0.1%
0.0348 3
< 0.1%
0.0349 1
 
< 0.1%
0.035 1
 
< 0.1%
0.0352 2
< 0.1%
ValueCountFrequency (%)
75.4135 1
 
< 0.1%
74.7083 1
 
< 0.1%
74.1305 3
 
< 0.1%
73.4487 3
 
< 0.1%
72.8899 4
 
< 0.1%
72.2303 13
 
< 0.1%
71.6896 11
 
< 0.1%
71.0511 20
< 0.1%
70.5276 26
< 0.1%
69.9094 33
< 0.1%

R14 (MOhm)
Real number (ℝ)

Distinct6193
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.978418
Minimum0.0314
Maximum108.6633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:50:57.519589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0314
5-th percentile0.1064
Q19.4998
median26.2795
Q343.4362
95-th percentile66.8445
Maximum108.6633
Range108.6319
Interquartile range (IQR)33.9364

Descriptive statistics

Standard deviation21.642733
Coefficient of variation (CV)0.77355098
Kurtosis-0.88103446
Mean27.978418
Median Absolute Deviation (MAD)16.9736
Skewness0.30567895
Sum8273749.8
Variance468.40788
MonotonicityNot monotonic
2022-12-20T13:50:57.672728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1072 2628
 
0.9%
0.1074 2578
 
0.9%
0.1071 2480
 
0.8%
0.107 2313
 
0.8%
0.1075 2296
 
0.8%
0.1078 2260
 
0.8%
0.1077 2200
 
0.7%
0.1079 2168
 
0.7%
0.1069 2128
 
0.7%
0.1067 2119
 
0.7%
Other values (6183) 272549
92.2%
ValueCountFrequency (%)
0.0314 1
 
< 0.1%
0.0319 1
 
< 0.1%
0.032 1
 
< 0.1%
0.0324 2
 
< 0.1%
0.0325 1
 
< 0.1%
0.0326 1
 
< 0.1%
0.0327 3
 
< 0.1%
0.0328 5
< 0.1%
0.0329 1
 
< 0.1%
0.033 8
< 0.1%
ValueCountFrequency (%)
108.6633 1
 
< 0.1%
86.9716 1
 
< 0.1%
85.2654 4
 
< 0.1%
84.3623 2
 
< 0.1%
83.6241 7
 
< 0.1%
82.7549 15
 
< 0.1%
82.0441 26
 
< 0.1%
81.2069 35
 
< 0.1%
80.522 67
< 0.1%
79.7151 90
< 0.1%

Interactions

2022-12-20T13:50:45.088979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:38.818192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:45.649809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:49.080552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:52.261445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:55.548636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:58.751532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:02.001337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:05.418575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:08.762127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:12.172611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:15.434756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:18.627759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:22.003817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:25.345591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:28.543664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:31.777805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:35.111960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:38.419337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:41.694427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:45.247963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:39.304437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:45.806361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:49.248823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:52.410949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:55.714313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:58.906486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:02.145332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:05.580364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:08.914935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:12.313896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:15.587260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:18.787916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:22.156080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:25.514339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:28.691578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:31.939807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:35.262327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:38.570844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:41.855938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:45.421618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:39.589835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:45.987211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:49.420066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:52.577216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:55.881722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:59.072984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:02.304683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:05.749604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:09.072421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:12.474687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:15.770716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:18.960460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:22.327988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:25.684812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:28.856680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:32.130071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:35.440005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:38.737245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:42.033209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:45.585833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:39.809337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:46.158625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:49.572542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:52.732842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:56.034475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:59.238215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:02.457342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:05.916480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:09.238902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:12.619917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:15.947015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:19.119679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:22.480942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:25.865983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:29.010976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:32.302130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:35.587747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:38.889956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:42.193047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:45.753180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:40.075015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:46.329243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:49.730677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:52.903154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:56.193225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:59.415153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:02.614472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:06.078403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:09.406755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:12.784771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:16.132042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:19.304451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:22.644037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:26.029547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:29.170729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:32.462799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:35.754701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:39.055522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:42.365599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:45.911765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:43.002165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:46.481607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:49.886328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:53.059092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:56.334520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:59.587979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:02.763121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:06.247858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:09.571493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:12.955901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:16.281766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:19.462133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:22.804319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:26.183003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:29.348947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:32.624597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:36.011285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:39.215469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:42.527101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:46.087835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:43.189148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:46.659198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:50.044384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:53.323505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:56.498050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:59.755117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:02.921299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:06.415554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:09.738086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:13.122339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:16.448576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:19.737044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:22.975084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:26.350525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:29.510046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:32.794787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:36.183236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:39.389490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:42.706884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:46.242957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:43.380563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:46.820131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:50.192963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:53.483564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:56.645773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:59.915013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:03.089780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:06.565759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:09.895712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:13.300899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:16.598757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:19.893285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:23.129578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:26.501746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:29.660243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:32.953457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:36.333455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:39.545322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:42.870515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:46.412832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:43.572640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:46.991298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:50.360769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:53.653974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:56.808549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-20T13:49:58.445212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:01.672717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:05.101132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:08.343775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:11.849271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:15.139731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:18.318294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:21.670919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:25.011717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:28.227145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:31.463878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:34.779215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:38.078711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:41.284534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:44.763397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:48.286052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:45.498997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:48.895047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:52.109126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:55.395785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:49:58.604731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:01.846292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:05.264687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:08.506280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:12.012986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:15.288671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:18.479186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:21.842462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:25.181814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:28.391695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:31.629529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:34.952246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:38.247267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:41.546896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:50:44.937608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-20T13:50:57.825665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-20T13:50:58.080312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-20T13:50:58.442297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-20T13:50:58.700673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-20T13:50:58.962671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-20T13:50:48.548328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-20T13:50:49.556822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
00.0000.049.753423.7184233.27370.89930.22310.63651.14930.84831.25341.44491.99061.33031.44801.91483.46515.21446.58068.6385
10.3090.055.840026.6200241.63230.21122.13145.35529.75696.31889.447210.576913.631721.982916.190224.278031.101434.719331.750541.9167
20.6180.055.840026.6200241.38880.207010.531822.561237.263517.784833.070436.316042.574649.749531.753357.728953.627556.921247.825562.9436
30.9260.055.840026.6200241.14610.204229.574949.511165.631826.144758.384767.513068.006459.282436.782166.083266.834966.969550.373064.8363
41.2340.055.840026.6200240.91210.203049.511167.036877.831727.962571.773279.947479.863162.538539.627168.144162.094749.461452.845366.8445
51.5440.055.840026.6200240.83610.202060.108374.344481.510029.797072.964383.147780.530258.041239.248265.098167.869763.531650.373063.3641
61.8540.055.840026.6200240.76020.201064.102074.344476.474828.179772.418178.436879.076859.761440.406764.141661.617360.500750.642466.2847
72.1630.055.840026.6200240.68450.200962.686971.387773.896527.652364.400769.791272.523955.636339.627162.798765.830756.519550.373064.3090
82.4720.055.840026.6200240.66240.200954.833667.036869.222827.442964.921163.905563.587254.073040.588164.141663.909053.081148.914663.3641
92.7810.055.840026.6200240.65190.200351.260064.530361.431225.688154.723557.211859.625555.636340.992861.907160.379152.788848.364062.4461
Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
29570990907.0030.062.326.580.10320.215.57649.036225.694619.348538.419239.297441.745662.538547.075360.577257.903853.081145.562867.8952
29571090907.3100.062.326.580.00000.212.75716.739322.011919.503841.376038.186341.126258.041247.617159.746264.335952.156744.361664.3090
29571190907.6200.062.326.580.00000.210.48925.098018.391418.933538.078338.383840.559558.423546.027261.040156.806851.818243.260663.8761
29571290907.9270.062.326.580.00000.28.48073.941515.218318.878238.957837.479140.735056.415846.027258.505257.556551.818243.906564.8363
29571390908.2360.062.326.580.00000.26.83913.131012.616418.438138.263537.479138.882953.355546.027261.040162.498053.081142.827264.3090
29571490908.5450.062.326.580.00000.25.54292.571310.381518.579636.458934.454938.374557.588845.795356.635156.405850.612943.023265.2822
29571590908.8530.062.326.580.00000.24.55272.14548.549418.059236.629034.005237.696451.975245.023958.937461.617350.936143.460463.8761
29571690909.1620.062.326.580.00000.23.73741.84927.106218.008736.012732.505637.188254.472445.023959.746257.145251.818242.594464.3090
29571790909.4690.062.326.580.00000.23.11971.61905.913817.695037.593030.525335.932851.975245.520157.728960.379150.346642.402262.0375
29571890909.7780.062.326.580.00000.22.64171.44094.939617.044835.125229.402435.202558.041243.140558.149856.076051.539341.985447.5339